20-database system architectures

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Database System Concepts, 5th Ed.

©Silberschatz, Korth and Sudarshan

See www.db-book.com for conditions on re-use

Chapter 20: Database System ArchitecturesChapter 20: Database System Architectures



©Silberschatz, Korth and Sudarshan20.2Database System Concepts - 5 th Edition, Oct 5, 2006

Chapter 20: Database System ArchitecturesChapter 20: Database System Architectures

Centralized and Client-Server Systems Server System Architectures

Parallel Systems

Distributed Systems

Network Types




Centralized SystemsCentralized Systems

Run on a single computer system and do not interact with other computer systems.

General-purpose computer system: one to a few CPUs and a number of device

controllers that are connected through a common bus that provides access to shared

memory.

Single-user system (e.g., personal computer or workstation): desk-top unit, single

user, usually has only one CPU and one or two hard disks; the OS may support only

one user.

Multi-user system: more disks, more memory, multiple CPUs, and a multi-user OS.

Serve a large number of users who are connected to the system vie terminals. Often

called server systems.




A Centralized Computer SystemA Centralized Computer System




ClientClient--Server SystemsServer Systems

Server systems satisfy requests generated at m client systems, whose general structure is

shown below:




ClientClient--Server Systems (Cont.)Server Systems (Cont.)

Database functionality can be divided into: Back-end: manages access structures, query evaluation and optimization, concurrency

control and recovery.

Front-end: consists of tools such as forms, report-writers, and graphical user interface

facilities.

The interface between the front-end and the back-end is through SQL or through an

application program interface.




ClientClient--Server Systems (Cont.)Server Systems (Cont.)

Advantages of replacing mainframes with networks of workstations or personal

computers connected to back-end server machines:

better functionality for the cost

flexibility in locating resources and expanding facilities

better user interfaces

easier maintenance




Server System ArchitectureServer System Architecture

Server systems can be broadly categorized into two kinds:

transaction servers which are widely used in relational database systems, and

data servers, used in object-oriented database systems




Transaction ServersTransaction Servers

Also called query server systems or SQL server systems

Clients send requests to the server

Transactions are executed at the server

Results are shipped back to the client.

Requests are specified in SQL, and communicated to the server through a remote

procedure call (RPC) mechanism. Transactional RPC allows many RPC calls to form a transaction.

Open Database Connectivity (ODBC) is a C language application program interface

standard from Microsoft for connecting to a server, sending SQL requests, and

receiving results.

JDBC standard is similar to ODBC, for Java




Transaction Server Process StructureTransaction Server Process Structure

A typical transaction server consists of multiple processes accessing data in shared

memory.

Server processes

These receive user queries (transactions), execute them and send results back

Processes may be multithreaded, allowing a single process to execute several

user queries concurrently

Typically multiple multithreaded server processes

Lock manager process

More on this later

Database writer process

Output modified buffer blocks to disks continually




Transaction Server Processes (Cont.)Transaction Server Processes (Cont.)

Log writer process

Server processes simply add log records to log record buffer

Log writer process outputs log records to stable storage.

Checkpoint process

Performs periodic checkpoints

Process monitor process

Monitors other processes, and takes recovery actions if any of the other processes fail

E.g. aborting any transactions being executed by a server process and restarting it




Transaction System Processes (Cont.)Transaction System Processes (Cont.)




Transaction System Processes (Cont.)Transaction System Processes (Cont.)

Shared memory contains shared data

Buffer pool

Lock table

Log buffer

Cached query plans (reused if same query submitted again)

All database processes can access shared memory

To ensure that no two processes are accessing the same data structure at the sametime, databases systems implement mutual exclusion using either

Operating system semaphores

Atomic instructions such as test-and-set

To avoid overhead of interprocess communication for lock request/grant, each

database process operates directly on the lock table

instead of sending requests to lock manager process

Lock manager process still used for deadlock detection




Data ServersData Servers

Used in high-speed LANs, in cases where

The clients are comparable in processing power to the server

The tasks to be executed are compute intensive.

Data are shipped to clients where processing is performed, and then shipped results

back to the server.

This architecture requires full back-end functionality at the clients. Used in many object-oriented database systems

Issues:

Page-Shipping versus Item-Shipping

Locking

Data Caching

Lock Caching




Data Servers (Cont.)Data Servers (Cont.)

Page-shipping versus item-shipping

Smaller unit of shipping more messages

Worth prefetching related items along with requested item

Page shipping can be thought of as a form of prefetching

Locking

Overhead of requesting and getting locks from server is high due to messagedelays

Can grant locks on requested and prefetched items; with page shipping,transaction is granted lock on whole page.

Locks on a prefetched item can be P{called back} by the server, and returned byclient transaction if the prefetched item has not been used.

Locks on the page can be deescalated to locks on items in the page when thereare lock conflicts. Locks on unused items can then be returned to server.




Data Servers (Cont.)Data Servers (Cont.)

Data Caching

Data can be cached at client even in between transactions

But check that data is up-to-date before it is used (cache coherency)

Check can be done when requesting lock on data item

Lock Caching

Locks can be retained by client system even in between transactions

Transactions can acquire cached locks locally, without contacting server

Server calls back locks from clients when it receives conflicting lock request. Client

returns lock once no local transaction is using it.

Similar to deescalation, but across transactions.




Parallel SystemsParallel Systems

Parallel database systems consist of multiple processors and multiple disks

connected by a fast interconnection network.

A coarse-grain parallel machine consists of a small number of powerful processors

A massively parallel or fine grain parallel machine utilizes thousands of smaller

processors.

Two main performance measures:

throughput --- the number of tasks that can be completed in a given time interval

response time --- the amount of time it takes to complete a single task from the

time it is submitted




SpeedSpeed--Up and ScaleUp and Scale--UpUp

Speedup: a fixed-sized problem executing on a small system is given to a system

which is N-times larger.

Measured by:

speedup = small system elapsed time

large system elapsed time

Speedup is linear if equation equals N. Scaleup: increase the size of both the problem and the system

N-times larger system used to perform N-times larger job

Measured by:

scaleup = small system small problem elapsed time

big system big problem elapsed time

Scale up is linear if equation equals 1.




SpeedupSpeedup

Speedup




ScaleupScaleup

Scaleup




Batch and Transaction ScaleupBatch and Transaction Scaleup

Batch scaleup:

A single large job; typical of most decision support queries and scientific

simulation.

Use an N-times larger computer on N-times larger problem.

Transaction scaleup:

Numerous small queries submitted by independent users to a shared database;typical transaction processing and timesharing systems.

N-times as many users submitting requests (hence, N-times as many requests)

to an N-times larger database, on an N-times larger computer.

Well-suited to parallel execution.




Factors Limiting Speedup and ScaleupFactors Limiting Speedup and Scaleup

Speedup and scaleup are often sublinear due to:

Startup costs: Cost of starting up multiple processes may dominate computation time,

if the degree of parallelism is high.

Interference: Processes accessing shared resources (e.g.,system bus, disks, or

locks) compete with each other, thus spending time waiting on other processes,

rather than performing useful work.

Skew: Increasing the degree of parallelism increases the variance in service times of parallely executing tasks. Overall execution time determined by slowest of parallely

executing tasks.




Interconnection Network ArchitecturesInterconnection Network Architectures

Bus. System components send data on and receive data from a single

communication bus;

Does not scale well with increasing parallelism.

Mesh. Components are arranged as nodes in a grid, and each component isconnected to all adjacent components

Communication links grow with growing number of components, and so scalesbetter.

But may require 2�n hops to sen d message to a n ode (or �n with wraparoun dconn ection s at edge of grid).

Hypercube. Components are numbered in binary; components are connected to oneanother if their binary representations differ in exactly one bit.

n components are connected to log(n) other components and can reach each

other via at most log(n) links; reduces communication delays.




Interconnection ArchitecturesInterconnection Architectures




Parallel Database ArchitecturesParallel Database Architectures

Shared memory -- processors share a common memory

Shared disk -- processors share a common disk

Shared nothing -- processors share neither a common memory nor common disk

Hierarchical -- hybrid of the above architectures




Shared MemoryShared Memory

Processors and disks have access to a common memory, typically via a bus or

through an interconnection network.

Extremely efficient communication between processors ² data in shared memory

can be accessed by any processor without having to move it using software.

Downside ± architecture is not scalable beyond 32 or 64 processors since the bus or

the interconnection network becomes a bottleneck

Widely used for lower degrees of parallelism (4 to 8).




Shared DiskShared Disk

All processors can directly access all disks via an interconnection network, but the

processors have private memories.

The memory bus is not a bottleneck

Architecture provides a degree of fault-tolerance ² if a processor fails, the other

processors can take over its tasks since the database is resident on disks that are

accessible from all processors.

Examples: IBM Sysplex and DEC clusters (now part of Compaq) running Rdb (now

Oracle Rdb) were early commercial users

Downside: bottleneck now occurs at interconnection to the disk subsystem.

Shared-disk systems can scale to a somewhat larger number of processors, but

communication between processors is slower.




HierarchicalHierarchical

Combines characteristics of shared-memory, shared-disk, and shared-nothing

architectures.

Top level is a shared-nothing architecture ± nodes connected by an interconnection

network, and do not share disks or memory with each other.

Each node of the system could be a shared-memory system with a few processors.

Alternatively, each node could be a shared-disk system, and each of the systems

sharing a set of disks could be a shared-memory system.

Reduce the complexity of programming such systems by distributed virtual-memory

architectures

Also called non-uniform memory architecture (NUMA)




Distributed SystemsDistributed Systems

Data spread over multiple machines (also referred to as sites or nodes).

Network interconnects the machines Data shared by users on multiple machines




Distributed DatabasesDistributed Databases

Homogeneous distributed databases

Same software/schema on all sites, data may be partitioned among sites

Goal: provide a view of a single database, hiding details of distribution

Heterogeneous distributed databases

Different software/schema on different sites

Goal: integrate existing databases to provide useful functionality

Differentiate between local and global transactions

A local transaction accesses data in the single site at which the transaction wasinitiated.

A global transaction either accesses data in a site different from the one at whichthe transaction was initiated or accesses data in several different sites.




TradeTrade--offs in Distributed Systemsoffs in Distributed Systems

Sharing data ± users at one site able to access the data residing at some other sites.

Autonomy ± each site is able to retain a degree of control over data stored locally.

Higher system availability through redundancy ² data can be replicated at remote

sites, and system can function even if a site fails.

Disadvantage: added complexity required to ensure proper coordination among sites.

Software development cost.

Greater potential for bugs.

Increased processing overhead.




Implementation Issues for Distributed DatabasesImplementation Issues for Distributed Databases

Atomicity needed even for transactions that update data at multiple sites

The two-phase commit protocol (2PC) is used to ensure atomicity

Basic idea: each site executes transaction until just before commit, and the leaves

final decision to a coordinator

Each site must follow decision of coordinator, even if there is a failure while waiting for

coordinators decision

2PC is not always appropriate: other transaction models based on persistent messaging,

and workflows, are also used

Distributed concurrency control (and deadlock detection) required

Data items may be replicated to improve data availability

Details of above in Chapter 22




Network TypesNetwork Types

Local-area networks (LANs) ± composed of processors that are distributed over small

geographical areas, such as a single building or a few adjacent buildings.

Wide-area networks (WANs) ± composed of processors distributed over a large

geographical area.




Networks Types (Cont.)Networks Types (Cont.)

WANs with continuous connection (e.g. the Internet) are needed for implementing

distributed database systems

Groupware applications such as Lotus notes can work on WANs with discontinuous

connection:

Data is replicated.

Updates are propagated to replicas periodically.

Copies of data may be updated independently.

Non-serializable executions can thus result. Resolution is application dependent.



Database System Concepts, 5th Ed.

©Silberschatz, Korth and SudarshanSee www.db-book.com for conditions on re-use

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20-database system architectures

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